


import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
    import torch

    from transformers import (
        MraForMaskedLM,
        MraForMultipleChoice,
        MraForQuestionAnswering,
        MraForSequenceClassification,
        MraForTokenClassification,
        MraModel,
    )
    from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST



class 				_UpperCamelCase			:

   '''simple docstring'''







   def __init__(	self	:			Optional[int]							,	_lowerCamelCase	:			Optional[int]							,	_lowerCamelCase	:			List[str]=2							,	_lowerCamelCase	:			int=8							,	_lowerCamelCase	:			int=True							,	_lowerCamelCase	:			List[str]=True							,	_lowerCamelCase	:			Union[str, Any]=True							,	_lowerCamelCase	:			Optional[int]=True							,	_lowerCamelCase	:			List[Any]=9_9							,	_lowerCamelCase	:			Dict=1_6							,	_lowerCamelCase	:			int=5							,	_lowerCamelCase	:			Any=2							,	_lowerCamelCase	:			Optional[int]=3_6							,	_lowerCamelCase	:			List[Any]="gelu"							,	_lowerCamelCase	:			int=0.0							,	_lowerCamelCase	:			str=0.0							,	_lowerCamelCase	:			List[str]=5_1_2							,	_lowerCamelCase	:			int=1_6							,	_lowerCamelCase	:			Any=2							,	_lowerCamelCase	:			Any=0.02							,	_lowerCamelCase	:			int=3							,	_lowerCamelCase	:			int=4							,	_lowerCamelCase	:			Optional[int]=None							,	):






         '''simple docstring'''



         __lowerCamelCase  :   Optional[Any]							    =    parent
         __lowerCamelCase  :   Union[str, Any]							    =    batch_size
         __lowerCamelCase  :   Tuple							    =    seq_length
         __lowerCamelCase  :   Optional[int]							    =    is_training
         __lowerCamelCase  :   str							    =    use_input_mask
         __lowerCamelCase  :   List[Any]							    =    use_token_type_ids
         __lowerCamelCase  :   List[Any]							    =    use_labels
         __lowerCamelCase  :   List[str]							    =    vocab_size
         __lowerCamelCase  :   Optional[int]							    =    hidden_size
         __lowerCamelCase  :   Tuple							    =    num_hidden_layers
         __lowerCamelCase  :   Dict							    =    num_attention_heads
         __lowerCamelCase  :   Optional[int]							    =    intermediate_size
         __lowerCamelCase  :   Tuple							    =    hidden_act
         __lowerCamelCase  :   int							    =    hidden_dropout_prob
         __lowerCamelCase  :   int							    =    attention_probs_dropout_prob
         __lowerCamelCase  :   List[str]							    =    max_position_embeddings
         __lowerCamelCase  :   Any							    =    type_vocab_size
         __lowerCamelCase  :   Optional[int]							    =    type_sequence_label_size
         __lowerCamelCase  :   int							    =    initializer_range
         __lowerCamelCase  :   Any							    =    num_labels
         __lowerCamelCase  :   Optional[Any]							    =    num_choices
         __lowerCamelCase  :   Optional[int]							    =    scope

   def 	_snake_case     (	self	:			Any				):






         '''simple docstring'''



         __lowerCamelCase  :   Optional[Any]							    =    ids_tensor([self.batch_size, self.seq_length]							,	self.vocab_size				)

         __lowerCamelCase  :   Union[str, Any]							    =    None
         if self.use_input_mask:
               __lowerCamelCase  :   str							    =    random_attention_mask([self.batch_size, self.seq_length]				)

         __lowerCamelCase  :   str							    =    None
         if self.use_token_type_ids:
               __lowerCamelCase  :   Tuple							    =    ids_tensor([self.batch_size, self.seq_length]							,	self.type_vocab_size				)

         __lowerCamelCase  :   str							    =    None
         __lowerCamelCase  :   Union[str, Any]							    =    None
         __lowerCamelCase  :   Optional[Any]							    =    None
         if self.use_labels:
               __lowerCamelCase  :   List[str]							    =    ids_tensor([self.batch_size]							,	self.type_sequence_label_size				)
               __lowerCamelCase  :   Optional[Any]							    =    ids_tensor([self.batch_size, self.seq_length]							,	self.num_labels				)
               __lowerCamelCase  :   Any							    =    ids_tensor([self.batch_size]							,	self.num_choices				)

         __lowerCamelCase  :   List[str]							    =    self.get_config()

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

   def 	_snake_case     (	self	:			Optional[int]				):






         '''simple docstring'''



         return MraConfig(
             vocab_size=self.vocab_size							,	hidden_size=self.hidden_size							,	num_hidden_layers=self.num_hidden_layers							,	num_attention_heads=self.num_attention_heads							,	intermediate_size=self.intermediate_size							,	hidden_act=self.hidden_act							,	hidden_dropout_prob=self.hidden_dropout_prob							,	attention_probs_dropout_prob=self.attention_probs_dropout_prob							,	max_position_embeddings=self.max_position_embeddings							,	type_vocab_size=self.type_vocab_size							,	is_decoder=lowerCamelCase_							,	initializer_range=self.initializer_range							,	)

   def 	_snake_case     (	self	:			List[str]				):






         '''simple docstring'''



         __lowerCamelCase  :   int							    =    self.get_config()
         __lowerCamelCase  :   str							    =    3_0_0
         return config

   def 	_snake_case     (	self	:			int				):






         '''simple docstring'''



         (
             (
             __lowerCamelCase     
         )    ,			(
             __lowerCamelCase     
         )    ,			(
             __lowerCamelCase     
         )    ,			(
             __lowerCamelCase     
         )    ,			(
             __lowerCamelCase     
         )    ,			(
             __lowerCamelCase     
         )    ,			(
             __lowerCamelCase     
         )    ,			     
         )  :   int							    =    self.prepare_config_and_inputs()

         __lowerCamelCase  :   Union[str, Any]							    =    True
         __lowerCamelCase  :   str							    =    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
         __lowerCamelCase  :   str							    =    ids_tensor([self.batch_size, self.seq_length]							,	vocab_size=2				)

         return (
             config,
             input_ids,
             token_type_ids,
             input_mask,
             sequence_labels,
             token_labels,
             choice_labels,
             encoder_hidden_states,
             encoder_attention_mask,
         )

   def 	_snake_case     (	self	:			Any							,	_lowerCamelCase	:			Tuple							,	_lowerCamelCase	:			int							,	_lowerCamelCase	:			Dict							,	_lowerCamelCase	:			Optional[Any]							,	_lowerCamelCase	:			List[Any]							,	_lowerCamelCase	:			int							,	_lowerCamelCase	:			str				):






         '''simple docstring'''



         __lowerCamelCase  :   Optional[Any]							    =    MraModel(config=lowerCamelCase_				)
         model.to(lowerCamelCase_				)
         model.eval()
         __lowerCamelCase  :   List[Any]							    =    model(lowerCamelCase_							,	attention_mask=lowerCamelCase_							,	token_type_ids=lowerCamelCase_				)
         __lowerCamelCase  :   Any							    =    model(lowerCamelCase_							,	token_type_ids=lowerCamelCase_				)
         __lowerCamelCase  :   List[Any]							    =    model(lowerCamelCase_				)
         self.parent.assertEqual(result.last_hidden_state.shape							,	(self.batch_size, self.seq_length, self.hidden_size)				)

   def 	_snake_case     (	self	:			Optional[int]							,	_lowerCamelCase	:			int							,	_lowerCamelCase	:			Tuple							,	_lowerCamelCase	:			Dict							,	_lowerCamelCase	:			int							,	_lowerCamelCase	:			Union[str, Any]							,	_lowerCamelCase	:			Optional[Any]							,	_lowerCamelCase	:			Any							,	_lowerCamelCase	:			List[Any]							,	_lowerCamelCase	:			List[str]							,	):






         '''simple docstring'''



         __lowerCamelCase  :   Union[str, Any]							    =    True
         __lowerCamelCase  :   Optional[Any]							    =    MraModel(lowerCamelCase_				)
         model.to(lowerCamelCase_				)
         model.eval()
         __lowerCamelCase  :   Tuple							    =    model(
             lowerCamelCase_							,	attention_mask=lowerCamelCase_							,	token_type_ids=lowerCamelCase_							,	encoder_hidden_states=lowerCamelCase_							,	encoder_attention_mask=lowerCamelCase_							,	)
         __lowerCamelCase  :   Dict							    =    model(
             lowerCamelCase_							,	attention_mask=lowerCamelCase_							,	token_type_ids=lowerCamelCase_							,	encoder_hidden_states=lowerCamelCase_							,	)
         __lowerCamelCase  :   int							    =    model(lowerCamelCase_							,	attention_mask=lowerCamelCase_							,	token_type_ids=lowerCamelCase_				)
         self.parent.assertEqual(result.last_hidden_state.shape							,	(self.batch_size, self.seq_length, self.hidden_size)				)

   def 	_snake_case     (	self	:			Union[str, Any]							,	_lowerCamelCase	:			int							,	_lowerCamelCase	:			Union[str, Any]							,	_lowerCamelCase	:			Optional[int]							,	_lowerCamelCase	:			Dict							,	_lowerCamelCase	:			Dict							,	_lowerCamelCase	:			Union[str, Any]							,	_lowerCamelCase	:			List[Any]				):






         '''simple docstring'''



         __lowerCamelCase  :   Union[str, Any]							    =    MraForMaskedLM(config=lowerCamelCase_				)
         model.to(lowerCamelCase_				)
         model.eval()
         __lowerCamelCase  :   Tuple							    =    model(lowerCamelCase_							,	attention_mask=lowerCamelCase_							,	token_type_ids=lowerCamelCase_							,	labels=lowerCamelCase_				)
         self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.seq_length, self.vocab_size)				)

   def 	_snake_case     (	self	:			Optional[int]							,	_lowerCamelCase	:			Dict							,	_lowerCamelCase	:			Union[str, Any]							,	_lowerCamelCase	:			Optional[Any]							,	_lowerCamelCase	:			Union[str, Any]							,	_lowerCamelCase	:			Optional[Any]							,	_lowerCamelCase	:			str							,	_lowerCamelCase	:			Tuple				):






         '''simple docstring'''



         __lowerCamelCase  :   Union[str, Any]							    =    MraForQuestionAnswering(config=lowerCamelCase_				)
         model.to(lowerCamelCase_				)
         model.eval()
         __lowerCamelCase  :   Tuple							    =    model(
             lowerCamelCase_							,	attention_mask=lowerCamelCase_							,	token_type_ids=lowerCamelCase_							,	start_positions=lowerCamelCase_							,	end_positions=lowerCamelCase_							,	)
         self.parent.assertEqual(result.start_logits.shape							,	(self.batch_size, self.seq_length)				)
         self.parent.assertEqual(result.end_logits.shape							,	(self.batch_size, self.seq_length)				)

   def 	_snake_case     (	self	:			Any							,	_lowerCamelCase	:			Any							,	_lowerCamelCase	:			Dict							,	_lowerCamelCase	:			List[str]							,	_lowerCamelCase	:			Tuple							,	_lowerCamelCase	:			Optional[Any]							,	_lowerCamelCase	:			int							,	_lowerCamelCase	:			List[str]				):






         '''simple docstring'''



         __lowerCamelCase  :   str							    =    self.num_labels
         __lowerCamelCase  :   Tuple							    =    MraForSequenceClassification(lowerCamelCase_				)
         model.to(lowerCamelCase_				)
         model.eval()
         __lowerCamelCase  :   int							    =    model(lowerCamelCase_							,	attention_mask=lowerCamelCase_							,	token_type_ids=lowerCamelCase_							,	labels=lowerCamelCase_				)
         self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.num_labels)				)

   def 	_snake_case     (	self	:			Union[str, Any]							,	_lowerCamelCase	:			Dict							,	_lowerCamelCase	:			List[Any]							,	_lowerCamelCase	:			Tuple							,	_lowerCamelCase	:			Dict							,	_lowerCamelCase	:			Union[str, Any]							,	_lowerCamelCase	:			Any							,	_lowerCamelCase	:			Tuple				):






         '''simple docstring'''



         __lowerCamelCase  :   Union[str, Any]							    =    self.num_labels
         __lowerCamelCase  :   List[str]							    =    MraForTokenClassification(config=lowerCamelCase_				)
         model.to(lowerCamelCase_				)
         model.eval()
         __lowerCamelCase  :   Any							    =    model(lowerCamelCase_							,	attention_mask=lowerCamelCase_							,	token_type_ids=lowerCamelCase_							,	labels=lowerCamelCase_				)
         self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.seq_length, self.num_labels)				)

   def 	_snake_case     (	self	:			Any							,	_lowerCamelCase	:			Optional[int]							,	_lowerCamelCase	:			Optional[Any]							,	_lowerCamelCase	:			Optional[Any]							,	_lowerCamelCase	:			List[Any]							,	_lowerCamelCase	:			str							,	_lowerCamelCase	:			Any							,	_lowerCamelCase	:			Dict				):






         '''simple docstring'''



         __lowerCamelCase  :   Dict							    =    self.num_choices
         __lowerCamelCase  :   Dict							    =    MraForMultipleChoice(config=lowerCamelCase_				)
         model.to(lowerCamelCase_				)
         model.eval()
         __lowerCamelCase  :   Tuple							    =    input_ids.unsqueeze(1				).expand(-1							,	self.num_choices							,	-1				).contiguous()
         __lowerCamelCase  :   str							    =    token_type_ids.unsqueeze(1				).expand(-1							,	self.num_choices							,	-1				).contiguous()
         __lowerCamelCase  :   List[Any]							    =    input_mask.unsqueeze(1				).expand(-1							,	self.num_choices							,	-1				).contiguous()
         __lowerCamelCase  :   Any							    =    model(
             lowerCamelCase_							,	attention_mask=lowerCamelCase_							,	token_type_ids=lowerCamelCase_							,	labels=lowerCamelCase_							,	)
         self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.num_choices)				)


   def 	_snake_case     (	self	:			str				):






         '''simple docstring'''



         __lowerCamelCase  :   Optional[int]							    =    self.prepare_config_and_inputs()
         (
             (
             __lowerCamelCase     
         )    ,			(
             __lowerCamelCase     
         )    ,			(
             __lowerCamelCase     
         )    ,			(
             __lowerCamelCase     
         )    ,			(
             __lowerCamelCase     
         )    ,			(
             __lowerCamelCase     
         )    ,			(
             __lowerCamelCase     
         )    ,			     
         )  :   str							    =    config_and_inputs
         __lowerCamelCase  :   Optional[int]							    =    {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
         return config, inputs_dict



@require_torch
class 				_UpperCamelCase			(    __lowerCAmelCase,unittest.TestCase    ):

   '''simple docstring'''







   a_					:				List[str]     =			(
       (
           MraModel,
           MraForMaskedLM,
           MraForMultipleChoice,
           MraForQuestionAnswering,
           MraForSequenceClassification,
           MraForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   a_					:				List[Any]     =			False
   a_					:				List[str]     =			False
   a_					:				int     =			False
   a_					:				Dict     =			False

   a_					:				List[str]     =			()

   def 	_snake_case     (	self	:			List[Any]				):






         '''simple docstring'''



         __lowerCamelCase  :   Any							    =    MraModelTester(self				)
         __lowerCamelCase  :   Union[str, Any]							    =    ConfigTester(self							,	config_class=lowerCamelCase_							,	hidden_size=3_7				)

   def 	_snake_case     (	self	:			List[Any]				):






         '''simple docstring'''



         self.config_tester.run_common_tests()

   def 	_snake_case     (	self	:			Union[str, Any]				):






         '''simple docstring'''



         __lowerCamelCase  :   int							    =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase_				)

   def 	_snake_case     (	self	:			Union[str, Any]				):






         '''simple docstring'''



         __lowerCamelCase  :   Optional[int]							    =    self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
               __lowerCamelCase  :   int							    =    type
               self.model_tester.create_and_check_model(*lowerCamelCase_				)

   def 	_snake_case     (	self	:			Union[str, Any]				):






         '''simple docstring'''



         __lowerCamelCase  :   List[Any]							    =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase_				)

   def 	_snake_case     (	self	:			List[Any]				):






         '''simple docstring'''



         __lowerCamelCase  :   List[str]							    =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase_				)

   def 	_snake_case     (	self	:			List[str]				):






         '''simple docstring'''



         __lowerCamelCase  :   Union[str, Any]							    =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_				)

   def 	_snake_case     (	self	:			Dict				):






         '''simple docstring'''



         __lowerCamelCase  :   Optional[Any]							    =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_				)

   def 	_snake_case     (	self	:			List[Any]				):






         '''simple docstring'''



         __lowerCamelCase  :   str							    =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_				)

   @slow
   def 	_snake_case     (	self	:			Union[str, Any]				):






         '''simple docstring'''



         for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __lowerCamelCase  :   int							    =    MraModel.from_pretrained(lowerCamelCase_				)
               self.assertIsNotNone(lowerCamelCase_				)


   @unittest.skip(reason="""MRA does not output attentions"""				)
   def 	_snake_case     (	self	:			Any				):






         '''simple docstring'''



         return



@require_torch
class 				_UpperCamelCase			(    unittest.TestCase    ):

   '''simple docstring'''







   @slow
   def 	_snake_case     (	self	:			int				):






         '''simple docstring'''



         __lowerCamelCase  :   Any							    =    MraModel.from_pretrained("""uw-madison/mra-base-512-4"""				)
         __lowerCamelCase  :   int							    =    torch.arange(2_5_6				).unsqueeze(0				)

         with torch.no_grad():
               __lowerCamelCase  :   Tuple							    =    model(lowerCamelCase_				)[0]

         __lowerCamelCase  :   int							    =    torch.Size((1, 2_5_6, 7_6_8)				)
         self.assertEqual(output.shape							,	lowerCamelCase_				)

         __lowerCamelCase  :   Union[str, Any]							    =    torch.tensor(
             [[[-0.0_140, 0.0_830, -0.0_381], [0.1_546, 0.1_402, 0.0_220], [0.1_162, 0.0_851, 0.0_165]]]				)

         self.assertTrue(torch.allclose(output[:, :3, :3]							,	lowerCamelCase_							,	atol=1E-4				)				)

   @slow
   def 	_snake_case     (	self	:			int				):






         '''simple docstring'''



         __lowerCamelCase  :   List[str]							    =    MraForMaskedLM.from_pretrained("""uw-madison/mra-base-512-4"""				)
         __lowerCamelCase  :   Dict							    =    torch.arange(2_5_6				).unsqueeze(0				)

         with torch.no_grad():
               __lowerCamelCase  :   Optional[Any]							    =    model(lowerCamelCase_				)[0]

         __lowerCamelCase  :   Optional[Any]							    =    5_0_2_6_5

         __lowerCamelCase  :   Optional[int]							    =    torch.Size((1, 2_5_6, vocab_size)				)
         self.assertEqual(output.shape							,	lowerCamelCase_				)

         __lowerCamelCase  :   Optional[Any]							    =    torch.tensor(
             [[[9.2_595, -3.6_038, 1_1.8_8_1_9], [9.3_869, -3.2_693, 1_1.0_9_5_6], [1_1.8_5_2_4, -3.4_938, 1_3.1_2_1_0]]]				)

         self.assertTrue(torch.allclose(output[:, :3, :3]							,	lowerCamelCase_							,	atol=1E-4				)				)


   @slow
   def 	_snake_case     (	self	:			str				):






         '''simple docstring'''



         __lowerCamelCase  :   List[str]							    =    MraForMaskedLM.from_pretrained("""uw-madison/mra-base-4096-8-d3"""				)
         __lowerCamelCase  :   str							    =    torch.arange(4_0_9_6				).unsqueeze(0				)

         with torch.no_grad():
               __lowerCamelCase  :   Dict							    =    model(lowerCamelCase_				)[0]

         __lowerCamelCase  :   Optional[int]							    =    5_0_2_6_5

         __lowerCamelCase  :   Tuple							    =    torch.Size((1, 4_0_9_6, vocab_size)				)
         self.assertEqual(output.shape							,	lowerCamelCase_				)

         __lowerCamelCase  :   str							    =    torch.tensor(
             [[[5.4_789, -2.3_564, 7.5_064], [7.9_067, -1.3_369, 9.9_668], [9.0_712, -1.8_106, 7.0_380]]]				)

         self.assertTrue(torch.allclose(output[:, :3, :3]							,	lowerCamelCase_							,	atol=1E-4				)				)




import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
     import torch

     from transformers.generation import (
         MaxLengthCriteria,
         MaxNewTokensCriteria,
         MaxTimeCriteria,
         StoppingCriteriaList,
         validate_stopping_criteria,
     )
@require_torch
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   def 						lowerCamelCase_	(     self						:						Tuple   ,   lowerCamelCase_						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      3
    UpperCamelCase			  =      250

    UpperCamelCase			  =      ids_tensor((batch_size, length)   ,   lowerCamelCase_				)
    UpperCamelCase			  =      torch.ones((batch_size, length)   ,   device=lowerCamelCase_   ,   dtype=torch.float				) / length
    return input_ids, scores







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)

    UpperCamelCase			  =      StoppingCriteriaList(
        [
            MaxLengthCriteria(max_length=10				),
            MaxTimeCriteria(max_time=0.1				),
        ]				)

    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(9				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(10				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      MaxLengthCriteria(max_length=10				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(9				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(10				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      MaxNewTokensCriteria(start_length=5   ,   max_new_tokens=5				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(9				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(10				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase			  =      StoppingCriteriaList([criteria]				)
    self.assertEqual(criteria_list.max_length   ,   10				)







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)

    UpperCamelCase			  =      MaxTimeCriteria(max_time=0.1				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase			  =      MaxTimeCriteria(max_time=0.1   ,   initial_timestamp=time.time() - 0.2				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)   ,   10				)

    with self.assertWarns(lowerCamelCase_				):
     validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)   ,   11				)

    UpperCamelCase			  =      validate_stopping_criteria(StoppingCriteriaList()   ,   11				)

    self.assertEqual(len(lowerCamelCase_				)   ,   1				)
